In Section REF , we analyse three state-of-the-art variants of the GBDT method found in the literature; namely, extreme gradient boosting (XGBoost) [1]}, light gradient boosting machine (LightGBM) [2]} and categorical boosting (CatBoost) [3]}, in detail. Later on in Section REF , we provide the background for error correcting output coding (ECOC) ensembles, on which we build our novel design strategies for designed ensembles of deep learning networks, presented in Section .
